Title: Product Category Classification in E-Commerce Clickstream Data Using Artificial Neural Networks
Authors: Taif J. Abu-Musabeh
Ahmad A. Musleh
Samy S. Abu-Naser
Volume: 10
Issue: 7
Pages: 1-8
Publication Date: 2026/07/28
Abstract:
The rapid growth of e-commerce platforms has generated large amounts of clickstream data that reflect customer browsing behavior. Analyzing this data can help businesses better understand customer interests and improve their online marketing strategies. This research explores the application of Artificial Neural Networks (ANNs) for product category classification using clickstream data collected from an online shopping website. The dataset contains information about customer interactions with products, including category, color, price, page location, and browsing activities. The data were preprocessed and transformed into a suitable format for neural network training. The ANN model was trained and validated to classify product categories based on user behavior patterns. The results indicate that Artificial Neural Networks are effective in identifying relationships within clickstream data and can provide accurate classification performance. This study demonstrates the potential of neural network techniques in supporting intelligent decision-making and enhancing e-commerce applications.